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- Sudre, C. H., et al.
(författare)
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Symptom clusters in COVID-19 : A potential clinical prediction tool from the COVID Symptom Study app
- 2021
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Ingår i: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 7:12
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Tidskriftsartikel (refereegranskat)abstract
- As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presenta- tions. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic – area under the curve) of need for respiratory sup- port was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.
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